Haisong Xu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Haisong Xu has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Anzhe Wang
China Electric Power Research Institute, Wuhan 430074, China
1 shared publication - Daning Zhang
Xi’an Jiaotong University
1 shared publication - Ge Wang
State Key Laboratory of Power Grid Environmental Protection
1 shared publication - Kaiwen Huang
China Electric Power Research Institute, Wuhan 430074, China
1 shared publication - Xu Lu
Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 518001, China
1 shared publication - Yuli Wang
China Electric Power Research Institute, Wuhan 430074, China
1 shared publication
Latest Publications
- Open Access
- 26 September 2024
- Published by MDPI AG in Electronics
- Vol. 13 (19) , 3807
Cable Insulation Defect Prediction Based on Harmonic Anomaly Feature Analysis
With the increasing demand for power supply reliability, online monitoring techniques for cable health condition assessments are gaining more attention. Most prevailing techniques lack the sensitivity needed to detect minor insulation defects. A new monitoring technique based on the harmonic anomaly feature analysis of the shield-to-ground current is introduced in this paper. The sensor installation and data acquisition are convenient and intrinsically safe, which makes it a preferred online monitoring technique. This study focuses on the single-core 10 kV distribution cable type. The research work includes the theoretical analysis of the cable defect’s impact on the current harmonic features, which are then demonstrated by simulation and lab experiments. It has been found that cable insulation defects cause magnetic field distortion, which introduces various harmonic current components, principally, the third-, fifth-, and seventh-order harmonic. The harmonic anomaly features are load current-, defect type-, and aging time-dependent. The K-means algorithm was selected as the data analysis algorithm and was used to achieve insulation defect prediction. The research outcome establishes a solid basis for the field application of the shield-to-ground harmonic current monitoring technique.
- 26 September 2024
- Published by MDPI AG in Electronics
- Vol. 13 (19) , 3807
Open AccessCable Insulation Defect Prediction Based on Harmonic Anomaly Feature Analysis
With the increasing demand for power supply reliability, online monitoring techniques for cable health condition assessments are gaining more attention. Most prevailing techniques lack the sensitivity needed to detect minor insulation defects. A new monitoring technique based on the harmonic anomaly feature analysis of the shield-to-ground current is introduced in this paper. The sensor installation and data acquisition are convenient and intrinsically safe, which makes it a preferred online monitoring technique. This study focuses on the single-core 10 kV distribution cable type. The research work includes the theoretical analysis of the cable defect’s impact on the current harmonic features, which are then demonstrated by simulation and lab experiments. It has been found that cable insulation defects cause magnetic field distortion, which introduces various harmonic current components, principally, the third-, fifth-, and seventh-order harmonic. The harmonic anomaly features are load current-, defect type-, and aging time-dependent. The K-means algorithm was selected as the data analysis algorithm and was used to achieve insulation defect prediction. The research outcome establishes a solid basis for the field application of the shield-to-ground harmonic current monitoring technique.